Artificial intelligence assisted clinical fluorescence imaging achieves in vivo cellular resolution comparable to adaptive optics ophthalmoscopy
The application of using AI as an add-on module for existing instrumentation is an important step towards routine screening and detection of disease at earlier stages.
Authors
- John Giannini (ORCID: https://orcid.org/0000-0001-7626-6540)
- Wadih M. Zein (ORCID: https://orcid.org/0000-0002-3771-6120)
- Nancy Aguilera (ORCID: https://orcid.org/0000-0003-0863-596X)
- Brian P. Brooks (ORCID: https://orcid.org/0000-0002-1916-7551)
- Johnny Tam (ORCID: https://orcid.org/0000-0003-2300-0567)
- Vineeta Das (ORCID: https://orcid.org/0000-0001-9114-1455)
- Joanne Li (ORCID: https://orcid.org/0000-0003-2845-2490)
- Sarah Abouassali
- Hong Le (ORCID: https://orcid.org/0000-0002-9477-9419)
- Laryssa A. Huryn (ORCID: https://orcid.org/0000-0002-0309-9419)
- A. I. Volkov
- Andrew J. Bower (ORCID: https://orcid.org/0000-0003-1645-5950)
- Rongwen Lu (ORCID: https://orcid.org/0000-0002-0224-7969)
- Emily Y. Chew (ORCID: https://orcid.org/0000-0003-0999-9802)
- Tao Liu (ORCID: https://orcid.org/0000-0001-9864-3896)
- Jianfei Liu (ORCID: https://orcid.org/0000-0001-9885-1695)
Institutions
- National Institutes of Health (US)
- National Eye Institute (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1038/s43856-025-00803-z
- Citations
- 5
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Institutes of Health
- National Eye Institute